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This paper investigates the long-term impacts of the federal Home Owners' Loan Corporation (HOLC) mortgage risk assessment maps on the spatial dynamics of recent income and racial distributions in California metropolitan areas over the 1990-2010 period. We combine historical HOLC boundaries with modern Census tract data and apply recently developed methods of spatial distribution dynamics to examine if legacy impacts are reflected in recent urban dynamics. Cities with HOLC assessments are found to have higher levels of isolation segregation than the non-HOLC group, but no difference in unevenness segregation between the two groups of cities are found. We find no difference in income or racial and ethnic distributional dynamics between the two groups of cities over the period. At the intra-urban scale, we find that the intersectionality of residing in a C or D graded tract that is also a low-income tract falls predominately upon the minority populations in these eight HOLC cities. Our findings indicate that neighborhoods with poor housing markets and high minority concentrations rarely experience a dramatic change in either their racial and ethnic or socioeconomic compositions—and that negative externalities (e.g. lower home prices and greater segregation levels) emanate from these neighborhoods, with inertia spilling over into nearby zones. [ABSTRACT FROM AUTHOR]

Big data, the "new oil" of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These "big code" practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy. [ABSTRACT FROM AUTHOR]

In this paper we examine the evolution of urban spatial structure in U.S. metropolitan areas over nearly two decades. Using annual block-level data from the Longitudinal Employment Household Dynamics database, we introduce a technique for identifying regional employment centers that both adheres to urban economic theory and pays homage to classic contributions in local spatial statistics. Centers are defined as local spatial statistical outliers on the network-based job accessibility surface. We proceed by identifying the location and employment makeup of centers for each metropolitan region in the USA from 2002 to 2019 and discuss emergent trends across time and space. Critically, we not only explore empirical patterns, but we discuss the relationship between polycentricity, the evolution of urbanization and localization economies, and regional specialization. We confirm again the pattern of polycentricity in U.S. metros and show that the structure of metropolitan employment is largely stable over time. We also document a continuing trend away from urbanization economies into more specialized subcenters. • We develop a new technique for identifying employment subcenters combining spatial statistics, network analysis, and computational geometry. • the technique generates bespoke polygons that can be used for further analysis or designated as targets for urban development. • We apply our method to 20 years of annual census block -level employment from LEHD and discuss the trend toward increasing polycentric development in the U.S. • We document the increasing prominence of more specialized localization economies. • The methodology we contribute is generalizeable and can be applied anywhere in the world (we provide a Python software package for reproducing the results) [ABSTRACT FROM AUTHOR]

Much progress has been made in the development of software tools for spatial analysis since the special issue of Geographical Analysis appeared in 2006, devoted to "Recent advances in software for spatial analysis in the social sciences" (Rey and Anselin 2006). The 15 some years since the publication of the issue have been marked by major changes in the spatial analytical software landscape. Arguably, three important and somewhat related phenomena can be distinguished that drove these changes: the embedding of spatial analysis into spatial data science; the growing recognition of open science/open source principles in empirical work; and the increasing adoption of a literate programming perspective. [ABSTRACT FROM AUTHOR]

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution. [ABSTRACT FROM AUTHOR]

Spatial data regularly suffer from error and uncertainty, ranging from poorly georeferenced coordinate pairs to sampling error associated with American Community Survey data. Geographic information systems can amplify and propagate error and uncertainty through the abstraction and representation of spatial data, as can the manipulation, processing, and analysis of spatial data using exploratory and confirmatory statistical techniques. The purpose of this article is to explore and address uncertainty in regionalization, a fundamental spatial analytical method that aggregates spatial units (e.g., tracts) into a set of contiguous regions for strategic purposes, including school districting, habitat areas, and the like. Specifically, we develop a new regionalization method, the uncertain‐max‐p‐regions problem that explicitly incorporates attribute uncertainty and allows its impacts to be evaluated with a degree of statistical certainty. We also detail an efficient solution approach for dealing the problem. The results suggest that the developed problem can out‐perform existing regionalization approaches and that the addition of a measure of statistical confidence can help to facilitate more clarity in planning and policy decisions. [ABSTRACT FROM AUTHOR]

The max‐p‐compact‐regions problem involves the aggregation of a set of small areas into an unknown maximum number (p) of compact, homogeneous, and spatially contiguous regions such that a regional attribute value is higher than a predefined threshold. The max‐p‐compact‐regions problem is an extension of the max‐p‐regions problem accounting for compactness. The max‐p‐regions model has been widely used to define study regions in many application cases since it allows users to specify criteria and then to identify a regionalization scheme. However, the max‐p‐regions model does not consider compactness even though compactness is usually a desirable goal in regionalization, implying ideal accessibility and apparent homogeneity. This article discusses how to integrate a compactness measure into the max‐p regionalization process by constructing a multiobjective optimization model that maximizes the number of regions while optimizing the compactness of identified regions. An efficient heuristic algorithm is developed to address the computational intensity of the max‐p‐compact‐regions problem so that it can be applied to large‐scale practical regionalization problems. This new algorithm will be implemented in the open‐source Python Spatial Analysis Library. One hypothetical and one practical application of the max‐p‐compact‐regions problem are introduced to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

geopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of 'best practice' in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to 'give back' whether in terms of how to teach individual concepts or deliver whole courses. As such, geopyter is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for geopyter, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like geopyter, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area. [ABSTRACT FROM AUTHOR]

AbstractModels of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population. [ABSTRACT FROM AUTHOR]

Empirical applications of the Markov chain model and its spatial extensions suffer from issues induced by the sparse transition probability matrix, which usually results from adopting maximum likelihood estimators (MLEs). Two discrete kernel estimators with cross‐validated parameters are proposed for reducing the sparsity in the estimated transition probability matrix. Monte Carlo experiments suggest that these estimators are not only quite effective in producing a much less sparse matrix, alleviating issues related to sparsity, but also superior to MLEs in terms of lowering the mean squared error for individual and total transition probability, giving rise to the better recovery of the underlying dynamics. [ABSTRACT FROM AUTHOR]

Neighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches to determine k endogenously. Existing approaches for neighborhood delineation that do not require pre-specification of a k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper considers the past and future of the journal Geographical Analysis (GA), as well as the broader field of spatial analysis. From my experiences as a former editor of GA, I first identify three external trends that I feel will provide the backdrop for the future evolution of spatial analysis. These surround the rise of artificial intelligence, machine learning, and data science as disciplines. The paper also considers the structural changes that are occurring in the organization of science, and the trend toward democratization of spatial analysis and spatial data. I identify critical areas for methodological advances along with some opportunities for the rebranding of GA in the new era of data science. [ABSTRACT FROM AUTHOR]

Regional income convergence and divergence has been an active field of research for more than 20 years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data have been sparsely developed. This study introduces a visual analytics system for the space–time analysis of income dynamics. We use state‐level US income data from 1929 to 2009 to demonstrate the visual analytics system and its utility for exploring similar data. The system consists of two modules, visualization and analytics. The visualization module, a Web‐based front‐end called Rank‐Path Visualizer (RPV), draws inspiration from the cartographic technique of flow mapping, originally developed by Tobler and embodied in his canonical Flow Mapper application. [ABSTRACT FROM AUTHOR]

Hypoxia is a universal feature of solid cancers caused by a mismatch between cellular oxygen supply and consumption. To meet the increased demand for oxygen, hypoxic cancer cells (CCs) induce a multifaceted process known as angiogenesis, wherein new vessels are formed by the sprouting of pre-existing ones. In addition to providing oxygen for growth and an exit route for dissemination, angiogenic vessels and factors are co-opted by CCs to enable the generation of an immunotolerant, hypoxic tumor microenvironment, leading to therapeutic failure and mortality. In this review, we discuss how hypoxia-inducible factors (HIFs), the mechanistic target of rapamycin (mTOR), and the unfolded protein response (UPR) control angiogenic factors serving both vascular and immunomodulatory functions in the tumor microenvironment. Possible therapeutic strategies, wherein targeting oxygen sensing might enhance anti-angiogenic and immunologically-mediated anti-cancer responses, are suggested. [ABSTRACT FROM AUTHOR]

The passing of Professor Arthur Getis in May of 2022 initiated a number of events to both reflect on and remember the tremendous contributions he has made over his career to geographical systems more broadly, but also spatial analysis, spatial statistics, regional science, geography and GIScience, among others. This began with a series of sessions at the North American Regional Science Council meetings held in Montreal, Canada in November of 2022, followed by an invitation from the editors of Journal of Geographical Systems, Manfred Fischer, Antonio Paez, and Petra Staufer-Steinnocher, to organize a special issue in his honor. Soon thereafter, an open call was initiated to solicit submissions to this special issue, seeking original contributions that overlap and complement the broad range of research undertaken by Professor Getis over a distinguished career and life. This paper offers an overview of prominent geographical systems work carried out by Professor Getis. Reflections on his many contributions are also detailed. A summary of the contributions to this special issue is given, along with final thoughts. [ABSTRACT FROM AUTHOR]

Unlike traditional geodemographic methods that do not incorporate neighborhood change into the delineation process, their method formulates the clustering problem as an integer optimization model to maximize the quality of model predictions regarding neighborhood change. Neighborhoods are increasingly central constructs in the social sciences, serving as spatiotemporal frames for the analysis of a host of questions. Despite the recent renewed interest in neighborhood effects and neighborhood dynamics, these two literatures remain largely separate. [Extracted from the article]

Stochastic dominance tests are used to measure whether distributions are directionally‐distinct. Using stochastic dominance measures, an income distribution can be measured to be more favorable for its members at all income levels than another distribution. This contrasts with conventional σ and β‐convergence approaches that compare distributions at a set of predetermined income levels. Given this comprehensiveness, stochastic dominance measures can yield novel insight into the relationship between income distributions, and thus to the relative economic welfare of inhabitants of different societies. However, stochastic dominance measures used in the analysis of regional income convergence have ignored the complications of geography. In this paper we examine the properties of common tests for stochastic dominance using various descriptive models of geographically‐realistic income growth processes. We find that the false positive rate of stochastic dominance tests increases significantly when comparing spatially‐informed income distributions growing over time. Further, this over‐eager rejection rate requires only that one distribution be spatially‐dependent, unlike some other bivariate statistics. We demonstrate that both the parametric and non‐parametric spatial filtering procedures are effective at resolving these issues in restricted circumstances for higher‐order dominance tests. Overall, while providing a novel and useful perspective on income distribution dynamics, stochastic dominance measures are demonstrated to have undesirable properties when used in geographically‐realistic scenarios and they resist one common method to address these issues. Solutions to the concerns expressed here remain open. [ABSTRACT FROM AUTHOR]

Abstract: The United Nations expressed an interest in reducing subnational (i.e., province and state level) inequality. We propose using a spatial decomposition of the Gini coefficient (SDGC) to track changes in subnational inequality. Typically, agencies do not track summary measures of subnational clustering of development indicators. Tracking changes in the SDGC can help measure and reduce regional inequality. To illustrate the use of the SDGC, we first present data for 93 nations to obtain cross‐sectional variation. Next, to illustrate how the SDGC trends over time, changes in the Human Development Index in Mongolia are compared to Russia and China. The SDGC can show improvement, decline and persistent clustering of subnational level inequality. The SDGC is a useful measure for the United Nations' Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Social and interregional inequality patterns across US states from 1929–2012 are analyzed using exploratory space–time methods. The results suggest complex spatial dynamics for both inequality series that were not captured by the stylized model of Alonso. Interpersonal income inequalities of states displayed a U-shaped pattern ending the period at levels that exceeded the alarmingly high patterns that existed in the 1920s. Social inequality is characterized by greater mobility than that found for state per capita incomes. Spatial dependence is also distinct between the two series, with per capita incomes exhibiting strong global spatial autocorrelation, while state interpersonal income inequality does not. Local hot and cold spots are found for the per capita income series, while local spatial outliers are found for state interpersonal inequality. Mobility in both inequality series is found to be influenced by the local spatial context of a state. [ABSTRACT FROM AUTHOR]

Molecular oxygen (O 2 ) is a universal electron acceptor that enables ATP synthesis through mitochondrial respiration in all metazoans. Consequently, hypoxia (low O 2 ) has arisen as an organizing principle for cellular evolution, metabolism, and (patho)biology, eliciting a remarkable panoply of metabolic adaptations that trigger transcriptional, translational, post-translational, and epigenetic responses to determine cellular fitness. In this review we summarize current and emerging cell-autonomous molecular mechanisms that induce hypoxic metabolic reprogramming in health and disease. [ABSTRACT FROM AUTHOR]

Understanding the impacts of land cover pattern on the heat island effect is essential for sustainable urban development. Conventional model fitting methods have restricted ability to produce accurate estimates of the land cover-temperature association due to the lack of procedures to address two important issues: spatial dependence in proximal spatial units and high correlations among predictor variables. In this study, we seek to develop an effective framework called spatially filtered ridge regression (SFRR) to estimate the variations in the quantity and distribution of land surface temperature (LST) in response to various land cover patterns. The SFRR effectively integrates spatial autoregressive models and ridge regression, and it achieves reliable parameter estimates with substantially reduced mean square errors. We show this by comparing the performance of the SFRR to other widely adopted models using Monte Carlo simulation followed by an empirical study over central Phoenix. Results highlight the great potential of the SFRR in producing accurate statistical estimates, providing a positive step toward informed and unbiased decision-making across a wide variety of disciplines. (Code and data to reproduce the results in the case study are available at: .) [ABSTRACT FROM AUTHOR]

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets. [ABSTRACT FROM AUTHOR]

Dissemination of breast cancer cells (BCCs) to distant sites (metastasis) is the ultimate cause of mortality in patients with breast cancer. Hypoxia (low O 2 ) is a microenvironmental hallmark of most solid cancers arising as a mismatch between cellular O 2 consumption and supply. Hypoxic selection of BCCs triggers molecular and cellular adaptations dependent upon hypoxia-inducible factors (HIFs), a family of evolutionarily conserved transcriptional activators that coordinate the expression of numerous genes controlling each step of the metastatic process. In this review, we summarize current advances in the understanding of HIF-driven molecular mechanisms that promote BCC metastatic dissemination and patient mortality. In addition, we discuss the clinical and therapeutic implications of HIF targeting in breast cancers. [ABSTRACT FROM AUTHOR]

In this paper we examine the relationship between spatial inequality, place mobility, and spatial polarization in Mexico. We use data for 2457 Mexican municipios from 2000 to 2015. We apply classical and spatial Markov chains to measure spatial income dynamics and we employ a decomposition of spatial inequality to measure spatial polarization. In doing so, we ask the following questions: Has spatial inequality increased across Mexican municipios since 2000? What has been the role of place mobility in any changes in overall spatial inequality? Finally, we examine what the main components of place mobility have been over this period. We find evidence of a strong spatial dependency between municipios that has increased over time but we find no clear pattern at the state scale. Intra-state municipal inequality has relatively declined while Inter-state municipal inequality polarization has increased. We also find clear evidence that transitional dynamics of municipio incomes are influenced by spatial context. Substantial evidence shows that municipio income mobility is driven by a growth component that could be likely reflecting the devaluation of the peso over this period. Exchange mobility is found to be a more important component of overall place mobility than dispersion (inequality) mobility. • Evidence of spatial heterogeneity with high income pockets throughout all regions. • Spatial poverty traps point towards the need to focus on pro-poor regional growth strategies. • Growth mobility accounts for the largest component of income dynamics followed by exchange mobility. [ABSTRACT FROM AUTHOR]

This article explores the stability of local vehicle ownership rates in Great Britain using the technique of spatial Markov chain analysis. Non-spatial Markov chain processes describe the transition of neighbourhoods through levels of ownership with no regard to their neighbourhood context. In reality however, how a neighbourhood transitions to different levels of ownership could be influenced by its neighbourhood context. A spatial Markov chain accounts for this context by estimating transition properties that are conditioned on the surrounding neighbourhood. These spatial Markov chain properties are estimated using a long run census time series from 1971 to 2011 of household vehicle ownership rates in Great Britain. These processes show that there is different behaviour in how neighbourhoods transition between levels of ownership depending on the context of their surrounding neighbours. The general finding is that the spatial Markov process will lead to a greater homogeneity in levels of ownership in each locality, with neighbourhoods surrounded by relatively low ownership neighbourhoods taking longer than a non-spatial Markov process would suggest to transition to higher levels, whilst neighbourhoods of high ownership surrounded by high ownership neighbourhoods take longer to transition to lower levels. This work corroborates Tobler's first law of geography “Everything is related to everything else, but near things are more related than distant things” but also provides practical guidance. Firstly, in modelling ownership, spatial effects need to be tested and when present, accounted for in the model formulation. Secondly, in a policy context, the surrounding neighbourhood situation is important, with neighbourhoods having a tendency towards homogeneity of ownership levels. This allows for the effective planning of transport provision for local services. Thirdly, vehicle ownership is often used as a proxy for the social and aspirational nature of an area and these results suggest that these properties will persist for a prolonged period, possibly perpetuating and exacerbating differentials in society. [ABSTRACT FROM AUTHOR]

Hypoxia (low O 2 ) is an essential microenvironmental driver of phenotypic diversity in human solid cancers. Hypoxic cancer cells hijack evolutionarily conserved, O 2 - sensitive pathways eliciting molecular adaptations that impact responses to radiotherapy, tumor recurrence and patient survival. In this review, we summarize the radiobiological, genetic, epigenetic and metabolic mechanisms orchestrating oncogenic responses to hypoxia. In addition, we outline emerging hypoxia- targeting strategies that hold promise for individualized cancer therapy in the context of radiotherapy and drug delivery. [ABSTRACT FROM AUTHOR]

Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series. [ABSTRACT FROM AUTHOR]

Segregated areas may occur around an attractive park or a waste incinerator, but the magnitude and group membership of the people in closest proximity will likely be difierent. We therefore introduce a local segregation measure that can be applied to any location within a metropolitan area, and that can identify the group that is relatively more concentrated around that reference location. We further introduce an inference approach to identify the statistical significance of a particular segregation value. In an exploratory setting the index can be used to generate a map of hot spots, and seed the question: 'why is this group significantly concentrated around that location?' [ABSTRACT FROM AUTHOR]

In the study of income inequality dynamics, the concept of exchange mobility plays a central role. Applications of classical rank correlation statistics have been used to assess the degree to which individual economies swap positions in the income distribution over time. These classic measures ignore the underlying geographical pattern of rank changes. Rey (2004) introduced a spatial concordance statistic as an extension of Kendall's rank correlation statistic, a commonly employed measure of exchange mobility. This article suggests local forms of the global spatial concordance statistic: local indicators of mobility association (LIMA). The LIMA statistics allow for the decomposition of the global measure into the contributions associated with individual locations. They do so by considering the degree of concordance (stability) or discordance (exchange mobility) reflected within an economy's local spatial context. Different forms of the LIMAs derive from alternative expressions of the neighborhood and neighbor set. Additionally, the additive decomposition of the LIMAs permits the development of a mesolevel analytic to examine whether the overall space–time concordance is driven by either interregional or intraregional concordance. The measures are illustrated in a case study that examines regional income dynamics in Mexico. [ABSTRACT FROM AUTHOR]

In this paper we examine the trajectory of regional income inequality dynamics for two neighboring national systems. Using data on 3038 US counties and 2418 Mexico municipios, from 2000, 2005, and 2010, we employ recent extensions of spatial Markov chains and space-time mobility measures, to consider the following questions: Are regional inequality dynamics fundamentally distinct between Mexico and the United States? Does the role of spatial context influence the distributional dynamics of the two systems? Finally we examine if there is a distinct international border region that displays inequality dynamics different from those of the internal regions of the two national systems. Strong evidence of spatial heterogeneity in regional income mobility is found between the two national systems, with Mexico having higher mobility relative to the US. The international border region is found to have distinct mobility dynamics from either national system, experiencing the strongest mobility. Extensive evidence of spatial contextual effects are found throughout the US-Mexican pooled data set indicating that a region's transitional dynamics are influenced by incomes of neighboring regions. [ABSTRACT FROM AUTHOR]

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms. [ABSTRACT FROM AUTHOR]

Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments. [ABSTRACT FROM PUBLISHER]

This paper presents a number of algorithms for a recently developed measure of space-time concordance. Based on a spatially explicit version of Kendall's $$\tau $$ the original implementation of the concordance measure relied on a brute force $$O(n^2)$$ algorithm which has limited its use to modest sized problems. Several new algorithms have been devised which move this run time to $$O(n log(n) +np)$$ where $$p$$ is the expected number of spatial neighbors for each unit. Comparative timing of these alternative implementations reveals dramatic efficiency gains in moving away from the brute force algorithms. A tree-based implementation of the spatial concordance is also found to dominate a merge sort implementation. [ABSTRACT FROM AUTHOR]

Markov chains have become a mainstay in the literature on regional income distribution dynamics and convergence. Despite its growing popularity, the Markov framework has some restrictive characteristics associated with the underlying discretization income distributions. This paper introduces several new approaches designed to mitigate some of the issues arising from discretization. Based on the examination of rank distributions, two new Markov-based chains are developed. The first explores the movement of individual economies through the income rank distribution over time. The second provides insight on the movements of ranks over geographical space and time. These also serve as the foundation for two new tests of spatial dynamics or the extent to which movements in the rank distribution are spatially clustered. An illustration of these new methods is included using income data for the lower 48 US states for the years 1929-2009. [ABSTRACT FROM AUTHOR]

Abstract: In addition to highlighting improvements in the availability and speed of broadband as a national priority, the National Broadband Plan also includes several recommendations for improving access and use of broadband by small businesses. The plan also recommends economic development officials include broadband in their local development strategies. While these are certainly import goals, more research is needed to evaluate how broadband impacts the regional business environment, and regional capacity to retain and attract businesses in particular industries. In order to further our understanding about the linkages between broadband and businesses, and the ability of places to retain and attract businesses in particular industries, this study will develop and estimate econometric models to better understand the linkages between broadband and firms in the knowledge intensive sector. Specifically, 54 metropolitan area specific models will be developed to examine regional variations in the linkages between broadband and firms in the knowledge intensive sector in 2004. Model results highlight the importance of broadband to knowledge firms in all but five metropolitan areas across the U.S. They also reveal variations in the impact of broadband on knowledge firm presence related to metropolitan area size and industrial legacy. [Copyright &y& Elsevier]

This paper introduces two new methods for the exploratory analysis of the spatial and temporal dynamics of residential burglary patterns. The first is a conditional spatial Markov chain which considers the extent to which a location's probability of experiencing a residential burglary in a future period is related to the prevalence of residential burglaries in its surrounding neighborhood in an initial period. The second measure extends this conditional perspective to examine the joint evolution of residential burglary in a location and its surrounding neighborhood. These methods are applied to a case study of residential burglary patterns in Mesa, Arizona over the period October 2005 through December 2009. Strong patterns of spatial clustering of burglary activity are present in each year, and this clustering is found to have an important influence on both the conditional and joint evolution of burglary activity across space and time. [ABSTRACT FROM AUTHOR]

Aims Hypoxia-inducible factor 1 (HIF-1) is a heterodimer composed of HIF-1α and HIF-1β subunits. HIF-1 is known to promote tissue vascularization by activating the transcription of genes encoding angiogenic factors, which bind to receptors on endothelial cells (ECs) and bone marrow-derived angiogenic cells (BMDACs). In this study, we analysed whether HIF-1 activity in the responding ECs and BMDACs is also required for cutaneous vascularization during burn wound healing. Methods and results We generated mice with floxed alleles at the Hif1a or Arnt locus encoding HIF-1α and HIF-1β, respectively. Expression of Cre recombinase was driven by the Tie2 gene promoter, which is expressed in ECs and bone marrow cells. Tie2Cre+ and Tie2Cre− mice were subjected to burn wounds of reproducible diameter and depth. Deficiency of HIF-1α or HIF-1β in Tie2-lineage cells resulted in delayed wound closure, reduced vascularization, decreased cutaneous blood flow, impaired BMDAC mobilization, and decreased BMDAC homing to burn wounds. Conclusion HIF-1 activity in Tie2-lineage cells is required for the mobilization and homing of BMDACs to cutaneous burn wounds and for the vascularization of burn wound tissue. [ABSTRACT FROM AUTHOR]

Abstract: Residential segregation is an inherently spatial phenomenon as it measures the separation of different types of people within a region. Whether measured with an explicitly spatial index, or a classic aspatial index, a region’s underlying spatial properties could manifest themselves in the magnitude of measured segregation. In this paper we implement a Monte Carlo simulation approach to investigate the properties of four segregation indices in regions built with specific spatial properties. This approach allows us to control the experiment in ways that empirical data do not. In general we confirm the expected results for the indices under various spatial properties, but some unexpected results emerge. Both the Dissimilarity Index and Neighborhood Sorting Index are sensitive to region size, but their spatial counterparts, the Adjusted Dissimilarity Index and Generalized Neighborhood Sorting Index, are generally immune to this problem. The paper also lends weight to concerns about the downward pressure on measured segregation when multiple neighborhoods are grouped into a single census tract. Finally, we discuss concerns about the way space is incorporated into segregation indices since the expected value of the spatial indices tested is lower than their aspatial counterparts. [Copyright &y& Elsevier]

The authors explore the effect of different topologies on properties of self-organizing maps (SOM). They suggest several diagnostics for measuring topology-induced errors in SOM and use these in a comparison of four different topologies. The results show that SOM is less sensitive to localized irregularities in the network structure than the literature may otherwise suggest. Further, the results support the use of spherical topologies as a solution to the boundary problem in traditional SOM. [ABSTRACT FROM AUTHOR]

The vascular system delivers oxygen and nutrients to every cell in the vertebrate organism. Hypoxia-inducible factor 1 (HIF-1) is a master regulator of hypoxic/ischaemic vascular responses, driving transcriptional activation of hundreds of genes involved in vascular reactivity, angiogenesis, arteriogenesis, and the mobilization and homing of bone marrow-derived angiogenic cells. This review will focus on the pivotal role of HIF-1 in vascular homeostasis, the involvement of HIF-1 in vascular diseases, and recent advances in targeting HIF-1 for therapy in preclinical models. [ABSTRACT FROM PUBLISHER]

lschemia induces the production of angiogenic cytokines and the homing of bone-marrow-derived angiogenic cells (BMDACs), but these adaptive responses become impaired with aging because of reduced expression of hypoxia-inducible factor (HlF)-1α. In this study, we analyzed the effect of augmenting HIF-1α levels in ischemic limb by intramuscular injection of AdCA5, an adenovirus encoding a constitutively active form of HIF-1α, and intravenous administration of BMDACs that were cultured in the presence of the prolyl-4-hydroxylase inhibitor dimethyloxalyiglycine (DMOG) to induce HIF-1 expression. The combined therapy increased perfusion. motor.function, and limb salvage in old mice subjected to femoral artery ligation. Homing of BMDACs to the ischemic limb was dramatically enhanced by intramuscular AdCA5 administration. DMOG treatment of BMDACs increased cell surface expression of β2 integrins. which mediated increased adherence of BMDACs to endothelial cells. The effect of DMOG was abolished by coadministration of the HIF-1 inhibitor digoxin or by preincubation with a 132 integrin-blocking antibody. Transduction of BMDACs with lentivirus LvCA5 induced effects similar to DMOG treatment. Thus, HIF-1α gene therapy increases homing of BMDACs to ischemic muscle, whereas HIF-1 induction in BMDAC5 enhances their adhesion to vascular endothelium, leading to synergistic effects of combined therapy on tissue perfusion. [ABSTRACT FROM AUTHOR]

This paper considers the intersection of academic spatial analysis with the open source revolution. Its basic premise is that the potential for cross-fertilization between the two is rich, yet some misperceptions about these two communities pose challenges to realizing these opportunities. The paper provides a primer on the open source movement for academicians with an eye towards correcting these misperceptions. It identifies a number of ways in which increased adoption of open source practices in spatial analysis can enhance the development of the next generation of tools and the wider practice of scientific research and education. [ABSTRACT FROM AUTHOR]

A key aspect in the study of regional disparities in Mexico is the potential leveling of economic and spatial inequalities. That original concern is associated with the sharpening of regional antagonisms and the generation of focuses of political and social tension. In this respect, a special interest exists to understand the spatial heterogeneity, i.e., as certain processes differ between regions and to what extent the spatial units —states, municipalities, cities, regions, etc —could mutually influence the adoption of certain social and economic practices, thus conditioning their dynamic response within the regional system. The analysis of these issues has been diluted in the literature under current methodological and theoretical biases. Although several studies in Mexico reflect a concern for spatial change, big lags exist due to a lack of consideration for spatially explicit methods of data analysis. [ABSTRACT FROM AUTHOR]

Amyloid-β plaques and neurodegeneration are hallmarks of Alzheimer's disease, where glial cells are responsible for sustained neuroinflammation. Here we show that hippocampal-microglia co-cultures exposed to proinflammatory mediators, amyloid-β- and amyloid-β protein precursor construct-conjugated beads increased their production of nitrites. In contrast, inflammation was unable to significantly induce cell death by itself, whereas inflammation plus amyloid-β or amyloid-β protein precursor induced a significant increment of cell death and a 6-fold increase of production of Interleukin 1β. Those effects were not observed in the absence of microglia or when hippocampal cells were co-cultured with microglia for one day. In contrast, a 2-fold increase of transforming growth factor β1 was observed in hippocampal cultures exposed to inflammatory stimuli for 4 days, whereas induction of transforming growth factor β1 by inflammation plus amyloid-β and amyloid-β protein precursor was nearly abolished by microglia. Our results indicate that neurotoxicity induced by amyloid-β or amyloid-β protein precursor was a slow process depending on activated microglia and additional stimuli. The observed cytotoxicity could be consequence of a vicious cycle in which elevated concentrations of Interleukin 1β and radical species along with decreased secretion of neuroprotective cytokines such as transforming growth factor β1 support persistent activation of glial cells and cell damage. [ABSTRACT FROM AUTHOR]

Chronic intermittent hypoxia (CIH) contributes to the development of hypertension in patients with obstructive sleep apnea and animal models. However, the early cardiovascular changes that precede CIH-induced hypertension are not completely understood. Nevertheless, it has been proposed that one of the possible contributing mechanisms to CIH-induced hypertension is a potentiation of carotid body (CB) hypoxic chemoreflexes. Therefore, we studied the dynamic responses of heart rate, blood pressure, and their variabilities during acute exposure to different levels of hypoxia after CIH short-term preconditioning (4 days) in cats. In addition, we measured baroreflex sensitivity (BRS) on the control of heart rate by noninvasive techniques. To assess the relationships among these indexes and CB chemoreflexes, we also recorded CB chemosensory discharges. Our data show that short-term CIH reduced BRS, potentiated the increase in heart rate induced by acute hypoxia, and was associated with a dynamic shift of heart rate variability (HRV) spectral indexes toward the low-frequency band. In addition, we found a striking linear correlation (r = 0.97) between the low-to-high frequency ratio of HRV and baseline. CB chemosensory discharges in the CIH-treated cats. Thus, our results suggest that cyclic hypoxic stimulation of the CB by short-term CIH induces subtle but clear selective alterations of HRV and BRS in normotensive cats. [ABSTRACT FROM AUTHOR]

PySAL is an open source library for spatial analysis written in the object-oriented language Python. It is built upon shared functionality in two exploratory spatial data analysis packages--GeoDA and STARS--and is intended to leverage the shared development of these components. This paper presents an overview of the motivation behind and the design of PySAL, as well as suggestions for how the library can be used with other software projects. Empirical illustrations of several key components in a variety of spatial analytical problems are given, and plans for future development of PySAL are discussed. [ABSTRACT FROM AUTHOR]

Background: In humans trophoblast invasion and vascular remodeling are critical to determine the fate of pregnancy. Since guinea-pigs share with women an extensive migration of the trophoblasts through the decidua and uterine arteries, and a haemomonochorial placenta, this species was used to evaluate the spatio-temporal expression of three enzymes that have been associated to trophoblast invasion, MMP-2, MMP-9 and tissue kallikrein (K1). Methods: Uteroplacental units were collected from early to term pregnancy. MMP-2, MMP-9 and K1 were analysed by immunohistochemistry and Western blot. The activities of MMP-2 and MMP-9 were assessed by gelatin zymography. Results: Immunoreactive MMP-2, MMP-9 and K1 were detected in the subplacenta, interlobar and labyrinthine placenta, syncytial sprouts and syncytial streamers throughout pregnancy. In late pregnancy, perivascular or intramural trophoblasts expressed the three enzymes. The intensity of the signal in syncytial streamers was increased in mid and late pregnancy for MMP-2, decreased in late pregnancy for MMP-9, and remained stable for K1. Western blots of placental homogenates at days 20, 40 and 60 of pregnancy identified bands with the molecular weights of MMP-2, MMP-9 and K1. MMP-2 expression remained constant throughout gestation. In contrast, MMP-9 and K1 attained their highest expression during midgestation. Placental homogenates of 20, 40 and 60 days yielded bands of gelatinase activity that were compatible with MMP-2 and MMP-9 activities. ProMMP-2 and MMP-9 activities did not vary along pregnancy, while MMP-2 and MMP-9 increased at 40 and 40-60 days respectively. Conclusion: The spatio-temporal expression of MMPs and K1 supports a relevant role of these proteins in trophoblast invasion, vascular remodeling and placental angiogenesis, and suggests a functional association between K1 and MMP-9 activation. [ABSTRACT FROM AUTHOR]

Abstract: Chronic intermittent hypoxia (CIH) enhances carotid body (CB) chemosensory responses to acute hypoxia. We tested the hypothesis that endothelin-1 (ET-1), an excitatory modulator of CB chemoreception may contribute to the enhanced CB chemosensory responses in cats exposed to cyclic hypoxic episodes repeated during 8 h for 4 days. Accordingly, we measured the ET-1 immunoreactivity (ET-ir) in the CB and plasma. Using a perfused CB preparation, we studied the effects of exogenous ET-1 and bosentan, a non-selective endothelin receptor type A and B antagonist, on the frequency of chemosensory discharges (ƒ x ) during normoxia, mild and severe hypoxia. We found that CIH increased ET-ir in the CB by ∼10-fold leaving ET-1 plasma levels unchanged. Application of ET-1 to control and CIH-treated CBs produced long-lasting dose-dependent increases in ƒ x , although the dose–response curve showed a rightward-shift in the CIH-treated CBs. CIH increased baseline ƒ x and hypoxic chemosensory responses, which were reduced by 50 μM bosentan in CBs from CIH-treated cats. Present results suggest that a local increase of ET-1 in the CB may contribute to the enhanced chemosensory responses induced by CIH predominantly through a vasomotor mechanism. [Copyright &y& Elsevier]

The article aims to explain regional economic development and economic convergence under the theme of spatial econometrics. The major idea behind the economic convergence is decreasing returns to capital accumulation. The economic convergence can be achieved by variety of theoretical and statistical models. There are so many advanced studies came out. They will facilitate convergence model specification and statistical testing for better efficiency.

This paper explores the implications that spatial effects can hold for the application of measures of σ-convergence. The bias of a common indicator of σ-convergence is examined for a family of spatial process models including: [a] spatial lag, [b] spatial error, and [c] spatial moving average. We show that the measure of σ-convergence is sensitive to a number of distinct influences including global dispersion, spatial dependence, and a variety of forms of spatial heterogeneity. We suggest a decomposition of the convergence indicator into two components: one reflecting global dispersion, and one reflecting the influence of spatial effects. We then illustrate this approach with a case study of the U.S. states over the 1929–2000 period. [ABSTRACT FROM AUTHOR]